Detection of Parkinson Disease Using Frequency Sub-bands of Vocal Cords Vibration Signals and Machine Learning Techniques

Abstract

In this study, we proposed a new approach for diagnosing Parkinson’s disease (PD) based on the slope values between neighboring amplitudes of vocal cord vibration signals. The inter-amplitude slope signals were obtained by computing the slopes between adjacent amplitudes in the vocal cord vibration signals. Feature vectors were extracted using common statistical parameters and applied to widely used machine learning classifiers such as Naive Bayes (NB), Generalized Logistic Regression (GLR), Logistic Regression (LR), Decision Tree (DT), and Random Forest (RFs). Different experiments were conducted to evaluate the contribution of the inter-amplitude slope approach and the performance of the classifiers in distinguishing healthy and PD segments. The experiments were carried out on original signals, inter-amplitude slope signals, and sub-band decompositions of both original and slope signals. The results showed satisfactory classification accuracy for all feature extraction methods, with the highest accuracy achieved using inter-amplitude slope signals. The GLR and Random Forest (RFs)-based classifiers outperformed others, achieving 100% accuracy, while the LR classifier reached 91%, and the DT and NB classifiers achieved 95%. Finally, the inter-amplitude slope approach, used for the first time in this study, enhanced classifier performance in PD diagnosis.

Keywords

• Vocal cords
• Parkinson
• inter-features slope

Ses Telleri Titreşim Sinyallerinin Frekans Alt Bantları ve Makine Öğrenimi Teknikleri Kullanılarak Parkinson Hastalığının TespitiDetection of Parkinson Disease Using Frequency Sub-Bands of Vocal Cords Vibration Signals and Machine Learning Techniques

Abstract

Bu çalışmada, ses teli titreşim sinyallerinin komşu genlikleri arasındaki eğim değerlerine dayanarak Parkinson hastalığının (PH) teşhisi için yeni bir yaklaşım önerdik. Genlikler arası eğim sinyalleri, ses teli titreşim sinyallerindeki komşu genlikler arasındaki eğimlerin hesaplanmasıyla elde edilmiştir. Özellik vektörleri ortak istatistiksel parametreler kullanılarak çıkarılmış ve Naive Bayes (NB), Genelleştirilmiş Lojistik Regresyon (GLR), Lojistik Regresyon (LR), Karar Ağacı (DT) ve Rastgele Orman (RFs) gibi yaygın olarak kullanılan makine öğrenimi sınıflandırıcılarına uygulanmıştır. Genlikler arası eğim yaklaşımının katkısını ve sınıflandırıcıların sağlıklı ve PD segmentlerini ayırt etmedeki performansını değerlendirmek için farklı deneyler yapılmıştır. Deneyler orijinal sinyaller, genlikler arası eğim sinyalleri ve hem orijinal hem de eğim sinyallerinin alt bant ayrıştırmaları üzerinde gerçekleştirilmiştir. Sonuçlar, tüm özellik çıkarma yöntemleri için tatmin edici sınıflandırma doğruluğu göstermiş ve en yüksek doğruluk genlikler arası eğim sinyalleri kullanılarak elde edilmiştir. GLR ve Rastgele Orman (RFs) tabanlı sınıflandırıcılar diğerlerinden daha iyi performans göstererek %100 doğruluk elde ederken, LR sınıflandırıcı %91'e, DT ve NB sınıflandırıcılar ise %95'e ulaşmıştır. Son olarak, bu çalışmada ilk kez kullanılan genlikler arası eğim yaklaşımı, PH teşhisinde sınıflandırıcı performansını artırmıştır.

Keywords

• Ses telleri
• Parkinson Hastalığı
• özellikler arası eğim

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